Dataset Information

Increase in Infant Measles Deaths During a Nationwide Measles Outbreak-Mongolia, 2015-2016.

ABSTRACT: BACKGROUND:Surveillance data from a large measles outbreak in Mongolia suggested increased case fatality ratio (CFR) in the second of 2 waves. To confirm the increase in CFR and identify risk factors for measles death, we enhanced mortality ascertainment and conducted a case-control study among infants hospitalized for measles. METHODS:We linked national vital records with surveillance data of clinically or laboratory-confirmed infant (aged <12 months) measles cases with rash onset during March-September 2015 (wave 1) and October 2015-June 2016 (wave 2). We abstracted medical charts of 95 fatal cases and 273 nonfatal cases hospitalized for measles, matched by age and sex. We calculated adjusted matched odds ratios (amORs) and 95% confidence intervals (CIs) for risk factors. RESULTS:Infant measles deaths increased from 3 among 2224 cases (CFR: 0.13%) in wave 1 to 113 among 4884 cases (CFR: 2.31%) in wave 2 (P < .001). Inpatient admission, 7-21 days before measles rash onset, for pneumonia or influenza (amOR: 4.5; CI, 2.6-8.0), but not other diagnoses, was significantly associated with death. DISCUSSION:Measles infection among children hospitalized with respiratory infections likely increased deaths due to measles during wave 2. Preventing measles virus nosocomial transmission likely decreases measles mortality.

SUBMITTER: Lee CT

PROVIDER: S-EPMC7034305 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increase in Infant Measles Deaths During a Nationwide Measles Outbreak-Mongolia, 2015-2016.

Lee Christopher T CT Hagan Jose E JE Jantsansengee Baigalmaa B Tumurbaatar Oyun-Erdene OE Altanchimeg Samdan S Yadamsuren Buyanjargal B Demberelsuren Sodbayar S Tserendorj Chinbayar C Munkhtogoo Oyungerel O Badarch Darmaa D Gunregjav Nyamaa N Baatarkhuu Bolortuya B Ochir Chimedsuren C Berman LaShondra L Anderson Raydel R Patel Minal K MK Gregory Christopher J CJ Goodson James L JL

The Journal of infectious diseases 20191001 11

<h4>Background</h4>Surveillance data from a large measles outbreak in Mongolia suggested increased case fatality ratio (CFR) in the second of 2 waves. To confirm the increase in CFR and identify risk factors for measles death, we enhanced mortality ascertainment and conducted a case-control study among infants hospitalized for measles.<h4>Methods</h4>We linked national vital records with surveillance data of clinically or laboratory-confirmed infant (aged <12 months) measles cases with rash onse ...[more]

PMID: 30923799

Similar Datasets

Project description:BACKGROUND:Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. OBJECTIVE:The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. METHODS:We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word "measles" posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. RESULTS:Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). CONCLUSIONS:The proposed scheme can successfully classify the public's opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.

Dataset Information

Increase in Infant Measles Deaths During a Nationwide Measles Outbreak-Mongolia, 2015-2016.

Publications

Increase in Infant Measles Deaths During a Nationwide Measles Outbreak-Mongolia, 2015-2016.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets